About me

Hi! My name is Lavender Jiang (蒋遥). I am a first year Data Science PhD student at New York University, co-advised by Eric Oermann and Kyunhyun Cho. I work on clinical predictions using natural language processing as a medical fellow for NYU Langone Health. I am interested in representation learning. I am a member with OLAB and ML2.

I received my BSc in Electrical and Computer Engineering and Mathematical Sciences from Carnegie Mellon, where I worked with José Moura on graph signal processing, Pulkit Grover on cortical spreading detection, and Howard Choset on sensor fusion.

Publications and Talks

  • NYUTron: Health System-scale Language Models for Clinical Operations: 30-day Readmissions. Lavender Y. Jiang, Nima P. Nejatian, Anthony B. Costa, Chris X. Liu, Yindalon Aphinyanaphongs, Mona G. Flores, Kyunghyun Cho, Eric K. Oermann. (NVIDIA GTC, 2022)

  • Automated, Scalable and Generalizable Deep Learning for Tracking Cortical Spreading Depression Using EEG. Alireza Chamanzar, Xujin Liu, Lavender Y. Jiang, Kimon A. Vogt, José M. F. Moura, Pulkit Grover. (International IEEE/EMBS Conference on Neural Engineering, 2021)

  • Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification. Lavender Y. Jiang, John Shi, Mark Cheung, Oren Wright, José M.F. Moura. (Proceeding of Asilomar Conference on Signals, Systems, and Computers 2020)

  • “Graph Signal Processing and Deep Learning”, Mark Cheung, John Shi, Yao Jiang, Oren Wright and José Moura. (IEEE Signal Processing Magazine Special Issue on Graph Signal Processing)

  • “Pooling in Graph Convolutional Neural Networks”, Mark Cheung, John Shi, Oren Wright, Yao Jiang and José Moura. (Proceeding of Asilomar Conference on Signals, Systems, and Computers 2019)